Parsing Pandas Series From Another Series

Question

Am trying to parse a series of text, using a series of numbers like the code below, but all i get in return is a series of NaN's.

import numpy as np
import pandas as pd
numData = np.array([4,6,4,3,6])
txtData = np.array(['bluebox','yellowbox','greybox','redbox','orangebox'])
n = pd.Series(numData)
t = pd.Series(txtData)
x = t.str[:n]
print (x)

output is

0   NaN
1   NaN
2   NaN
3   NaN
4   NaN

I would like the output to be

0      blue
1    yellow
2      grey
3       red
4    orange

Is there an easy way to do this.

Answer 1

You can use a simple list comprehension if in reality you can't chop off the last 3 characters and need to rely on your slice ranges. You will need error handling if your data aren't guaranteed to be all strings, or if end can exceed the length of the string.

pd.Series([x[:end] for x,end in zip(t,n)], index=t.index)

0      blue
1    yellow
2      grey
3       red
4    orange
dtype: object

Answer 2

You can pd.Series.str.slice

t.str.slice(stop=-3)
# short hand for this is t.str[:-3]
0      blue
1    yellow
2      grey
3       red
4    orange
dtype: object

Or cast numData as an iterator using iter and use slice

it = iter(numData)
t.map(lambda x:x[slice(next(it))])
0      blue
1    yellow
2      grey
3       red
4    orange
dtype: object

Answer 3

numdata_iter = iter(numData)
x = t.apply(lambda text: text[:next(numdata_iter)])

We turn the numData into an iterator and then invoke next on it for each slicing in apply.